@article {1261, title = {A genome-wide association study identifies novel loci associated with circulating IGF-I and IGFBP-3.}, journal = {Hum Mol Genet}, volume = {20}, year = {2011}, month = {2011 Mar 15}, pages = {1241-51}, abstract = {

Insulin-like growth factor-I (IGF-I) and insulin-like growth factor-binding protein-3 (IGFBP-3) are involved in cell replication, proliferation, differentiation, protein synthesis, carbohydrate homeostasis and bone metabolism. Circulating IGF-I and IGFBP-3 concentrations predict anthropometric traits and risk of cancer and cardiovascular disease. In a genome-wide association study of 10 280 middle-aged and older men and women from four community-based cohort studies, we confirmed a known association of single nucleotide polymorphisms in the IGFBP3 gene region on chromosome 7p12.3 with IGFBP-3 concentrations using a significance threshold of P < 5 {\texttimes} 10(-8) (P = 3.3 {\texttimes} 10(-101)). Furthermore, the same IGFBP3 gene locus (e.g. rs11977526) that was associated with IGFBP-3 concentrations was also associated with the opposite direction of effect, with IGF-I concentration after adjustment for IGFBP-3 concentration (P = 1.9 {\texttimes} 10(-26)). A novel and independent locus on chromosome 7p12.3 (rs700752) had genome-wide significant associations with higher IGFBP-3 (P = 4.4 {\texttimes} 10(-21)) and higher IGF-I (P = 4.9 {\texttimes} 10(-9)) concentrations; when the two measurements were adjusted for one another, the IGF-I association was attenuated but the IGFBP-3 association was not. Two additional loci demonstrated genome-wide significant associations with IGFBP-3 concentration (rs1065656, chromosome 16p13.3, P = 1.2 {\texttimes} 10(-11), IGFALS, a confirmatory finding; and rs4234798, chromosome 4p16.1, P = 4.5 {\texttimes} 10(-10), SORCS2, a novel finding). Together, the four genome-wide significant loci explained 6.5\% of the population variation in IGFBP-3 concentration. Furthermore, we observed a borderline statistically significant association between IGF-I concentration and FOXO3 (rs2153960, chromosome 6q21, P = 5.1 {\texttimes} 10(-7)), a locus associated with longevity. These genetic loci deserve further investigation to elucidate the biological basis for the observed associations and clarify their possible role in IGF-mediated regulation of cell growth and metabolism.

}, keywords = {Aged, Chromosomes, Human, Pair 7, Cohort Studies, European Continental Ancestry Group, Female, Genome-Wide Association Study, Humans, Insulin-Like Growth Factor Binding Protein 3, Insulin-Like Growth Factor I, Male, Polymorphism, Single Nucleotide}, issn = {1460-2083}, doi = {10.1093/hmg/ddq560}, author = {Kaplan, Robert C and Petersen, Ann-Kristin and Chen, Ming-Huei and Teumer, Alexander and Glazer, Nicole L and D{\"o}ring, Angela and Lam, Carolyn S P and Friedrich, Nele and Newman, Anne and M{\"u}ller, Martina and Yang, Qiong and Homuth, Georg and Cappola, Anne and Klopp, Norman and Smith, Holly and Ernst, Florian and Psaty, Bruce M and Wichmann, H-Erich and Sawyer, Douglas B and Biffar, Reiner and Rotter, Jerome I and Gieger, Christian and Sullivan, Lisa S and V{\"o}lzke, Henry and Rice, Kenneth and Spyroglou, Ariadni and Kroemer, Heyo K and Ida Chen, Y-D and Manolopoulou, Jenny and Nauck, Matthias and Strickler, Howard D and Goodarzi, Mark O and Reincke, Martin and Pollak, Michael N and Bidlingmaier, Martin and Vasan, Ramachandran S and Wallaschofski, Henri} } @article {9745, title = {Development and validation of a machine learning-based approach to identify high-risk diabetic cardiomyopathy phenotype.}, journal = {Eur J Heart Fail}, year = {2024}, month = {2024 Sep 06}, abstract = {

AIMS: Abnormalities in specific echocardiographic parameters and cardiac biomarkers have been reported among individuals with diabetes. However, a comprehensive characterization of diabetic cardiomyopathy (DbCM), a subclinical stage of myocardial abnormalities that precede the development of clinical heart failure (HF), is lacking. In this study, we developed and validated a machine learning-based clustering approach to identify the high-risk DbCM phenotype based on echocardiographic and cardiac biomarker parameters.

METHODS AND RESULTS: Among individuals with diabetes from the Atherosclerosis Risk in Communities (ARIC) cohort who were free of cardiovascular disease and other potential aetiologies of cardiomyopathy (training, n = 1199), unsupervised hierarchical clustering was performed using echocardiographic parameters and cardiac biomarkers of neurohormonal stress and chronic myocardial injury (total 25 variables). The high-risk DbCM phenotype was identified based on the incidence of HF on follow-up. A deep neural network (DeepNN) classifier was developed to predict DbCM in the ARIC training cohort and validated in an external community-based cohort (Cardiovascular Health Study [CHS]; n = 802) and an electronic health record (EHR) cohort (n = 5071). Clustering identified three phenogroups in the derivation cohort. Phenogroup-3 (n = 324, 27\% of the cohort) had significantly higher 5-year HF incidence than other phenogroups (12.1\% vs. 4.6\% [phenogroup 2] vs. 3.1\% [phenogroup 1]) and was identified as the high-risk DbCM phenotype. The key echocardiographic predictors of high-risk DbCM phenotype were higher NT-proBNP levels, increased left ventricular mass and left atrial size, and worse diastolic function. In the CHS and University of Texas (UT) Southwestern EHR validation cohorts, the DeepNN classifier identified 16\% and 29\% of participants with DbCM, respectively. Participants with (vs. without) high-risk DbCM phenotype in the external validation cohorts had a significantly higher incidence of HF (hazard ratio [95\% confidence interval] 1.61 [1.18-2.19] in CHS and 1.34 [1.08-1.65] in the UT Southwestern EHR cohort).

CONCLUSION: Machine learning-based techniques may identify 16\% to 29\% of individuals with diabetes as having a high-risk DbCM phenotype who may benefit from more aggressive implementation of HF preventive strategies.

}, issn = {1879-0844}, doi = {10.1002/ejhf.3443}, author = {Segar, Matthew W and Usman, Muhammad Shariq and Patel, Kershaw V and Khan, Muhammad Shahzeb and Butler, Javed and Manjunath, Lakshman and Lam, Carolyn S P and Verma, Subodh and Willett, DuWayne and Kao, David and Januzzi, James L and Pandey, Ambarish} }